Search Results (4,719)

Objective: This scoping review identifies and analyzes factors influencing the effectiveness of orthodontic mini-implants and temporary anchorage devices in orthodontic treatments, including clinical applications, success rates, and associated complications. Methods: A systematic search was conducted across EBSCOhost, Ovid Medline, PubMed, Scopus, and Web of Science for peer-reviewed, English-language human studies published between 2013 and 2023 that examined determinants of mini-implants/temporary anchorage devices success or failure. Inclusion/exclusion criteria were predefined, and screening was performed in duplicate. Thirty-six studies met criteria. Results: Placement site and peri-implant oral hygiene/soft-tissue health were the most consistent contributors to success. Optimal sites varied by indication, supporting individualized planning. Greater implant length generally improved stability but must be balanced against anatomic limits and patient comfort. Temporary anchorage devices supported diverse movements (e.g., molar distalization; posterior/anterior intrusion). Findings for loading protocol, patient age, bone quality, and operator experience were mixed, reflecting heterogeneity in primary stability, force magnitude/vector, and outcome definitions. Conclusion: Mini-implants/temporary anchorage devices success is multifactorial. Emphasis on site-specific selection, hygiene management, appropriate implant dimensions, and patient-specific modifiers can optimize outcomes and minimize complications. Future studies should report standardized outcomes and explicit loading parameters to enable granular analyses of movement-specific biomechanics and evidence-based decision-making. Full article

Artificial intelligence (AI) has emerged as a transformative technology in radiology, offering enhanced diagnostic accuracy, improved workflow efficiency and potential risk mitigation. However, its effectiveness in reducing clinical and occupational risks in radiology departments remains underexplored. This systematic review aimed to evaluate the current literature on AI applications in computed tomography (CT) radiology and their contributions to risk reduction. Following the PRISMA 2020 guidelines, a systematic search was conducted in PubMed, Scopus and Web of Science for studies published between 2021 and 2025 (the databases were last accessed on 15 April 2025). Thirty-four studies were included based on their relevance to AI in radiology and reported outcomes. Extracted data included study type, geographic region, AI application and type, role in clinical workflow, use cases, sensitivity and specificity. The majority of studies addressed triage (61.8%) and computer-aided detection (32.4%). AI was most frequently applied in chest imaging (47.1%) and brain haemorrhage detection (29.4%). The mean reported sensitivity was 89.0% and specificity was 93.3%. AI tools demonstrated advantages in image interpretation, automated patient positioning, prioritisation and measurement standardisation. Reported benefits included reduced cognitive workload, improved triage efficiency, decreased manual annotation and shorter exposure times. AI systems in CT radiology show strong potential to enhance diagnostic consistency and reduce occupational risks. The evidence supports the integration of AI-based tools to assist diagnosis, lower human workload and improve overall safety in radiology departments. Full article

Childhood obesity has emerged as one of the most pressing public health challenges of the 21st century. Early-onset obesity is associated with an increased risk of developing numerous comorbidities later in life. Despite extensive research into its multifactorial etiology—including genetic, behavioral, environmental, and socioeconomic factors—the precise molecular mechanisms underlying the development and persistence of obesity in the pediatric population remain incompletely understood. Proteomics offers promising insights into these mechanisms. The application of proteomics in pediatric obesity research has grown, enabling the identification of proteins that reflect dynamic changes in metabolic and inflammatory pathways. This advancement allows clinicians to move beyond traditional anthropometric measurements toward personalized approaches with notification of early complications of obesity. A systematic search was conducted across PubMed, Scopus, and Web of Science for studies published between 2010 and 2025. Inclusion criteria: human studies, participants aged 0–18, proteomic analysis of obesity, and biomarkers. Data extraction and quality assessment followed standardized protocols. From 239 articles, 20 were included. Key dysregulated proteins include APOA1, CLU, and HP. LC-MS/MS was the predominant technique used. Some biomarkers were predictive for obesity complications in children. Proteomics holds clinical potential for early detection and personalized treatment of pediatric obesity. Standardized methodologies and longitudinal studies are needed for translation into clinical practice. Full article

Background: Occupational risk prevention is a critical discipline for ensuring safe working conditions and minimizing accidents and occupational diseases. With the rise of artificial intelligence (AI) and machine learning (ML), these approaches are increasingly utilized for predicting and preventing workplace hazards. This systematic review aims to identify, evaluate, and synthesize existing literature on the use of AI algorithms for detecting and predicting hazardous environments and occupational risks in the workplace, focusing on predictive modeling and prevention strategies. Methods: A systematic literature review was conducted following the PRISMA 2020 protocol, with minor adaptations to include conference proceedings and technical reports due to the topic’s emerging and multidisciplinary nature. Searches were performed in IEEE Digital Library, PubMed, Scopus, and Web of Science, with the last search conducted on 1 August 2024. Only peer-reviewed articles published from 2019 onwards and written in English were included. Systematic literature reviews were explicitly excluded. The screening process involved duplicate removal (reducing 209 initial documents to 183 unique ones), a preliminary screening based on titles, abstracts, and keywords (further reducing to 92 articles), and a detailed full-text review. During the full-text review, study quality was assessed using six quality assessment (QA) questions, where articles receiving a total score below 4.5 or 0 in any QA question were excluded. This rigorous process resulted in the selection of 61 relevant articles for quantitative and qualitative analysis. Results: The analysis revealed a growing interest in the field, with a clear upward trend in publications from 2021 to 2023, and a continuation of growth into 2024. The most significant contributions originated from countries such as China, South Korea, and India. Applications primarily focused on high-risk sectors, notably construction, mining, and manufacturing. The most common approach involved the use of visual data captured by cameras, which constituted over 40% of the reviewed studies, processed using deep learning (DL) models, particularly Convolutional Neural Networks (CNNs) and You Only Look Once (YOLO). Conclusions: The study highlights current limitations, including an over-reliance on visual data (especially challenging in low-visibility environments) and a lack of methodological standardization for AI-based risk detection systems. Future research should emphasize the integration of multimodal data (visual, environmental, physiological) and the development of interpretable AI models (XAI) to enhance accuracy, transparency, and trust in hazard detection systems. Addressing long-term societal implications, such as privacy and potential worker displacement, necessitates transparent data policies and robust regulatory frameworks. Full article

Background/Objectives: This systematic review aims to synthesize recent studies on the integration of artificial intelligence (AI) into robotic surgery for oncological patients. It focuses on studies using real patient data and AI tools in robotic oncologic surgery. Methods: This systematic review followed PRISMA guidelines to ensure a robust methodology. A comprehensive search was conducted in June 2025 across Embase, Medline, Web of Science, medRxiv, Google Scholar, and IEEE databases, using MeSH terms, relevant keywords, and Boolean logic. Eligible studies were original research articles published in English between 2024 and 2025, focusing on AI applications in robotic cancer surgery using real patient data. Studies were excluded if they were non-peer-reviewed, used synthetic/preclinical data, addressed non-oncologic indications, or explored non-robotic AI applications. This approach ensured the selection of studies with practical clinical relevance. Results: The search identified 989 articles, with 17 duplicates removed. After screening, 921 were excluded, and 37 others were eliminated for reasons such as misalignment with inclusion criteria or lack of full text. Ultimately, 14 articles were included, with 8 using a retrospective design and 6 based on prospective data. These included articles that varied significantly in terms of the number of participants, ranging from several dozen to several thousand. These studies explored the application of AI across various stages of robotic oncologic surgery, including preoperative planning, intraoperative support, and postoperative predictions. The quality of 11 included studies was very good and good. Conclusions: AI significantly supports robotic oncologic surgery at various stages. In preoperative planning, it helps estimate the risk of conversion from minimally invasive to open colectomy in colon cancer. During surgery, AI enables precise tumor and vascular structure localization, enhancing resection accuracy, preserving healthy tissue, and reducing warm ischemia time. Postoperatively, AI’s flexibility in predicting functional and oncological outcomes through context-specific models demonstrates its value in improving patient care. Due to the relatively small number of cases analyzed, further analysis of the issues presented in this review is necessary. Full article

Background: Artificial intelligence (AI) is increasingly applied in the diagnosis of pediatric rare diseases, enhancing the speed, accuracy, and accessibility of genetic interpretation. These advances support the ongoing shift toward personalized medicine in clinical genetics. Objective: This review examines current applications of AI in pediatric rare disease diagnostics, with a particular focus on real-world data integration and implications for individualized care. Methods: A narrative review was conducted covering AI tools for variant prioritization, phenotype–genotype correlations, large language models (LLMs), and ethical considerations. The literature was identified through PubMed, Scopus, and Web of Science up to July 2025, with priority given to studies published in the last seven years. Results: AI platforms provide support for genomic interpretation, particularly within structured diagnostic workflows. Tools integrating Human Phenotype Ontology (HPO)-based inputs and LLMs facilitate phenotype matching and enable reverse phenotyping. The use of real-world data enhances the applicability of AI in complex and heterogeneous clinical scenarios. However, major challenges persist, including data standardization, model interpretability, workflow integration, and algorithmic bias. Conclusions: AI has the potential to advance earlier and more personalized diagnostics for children with rare diseases. Achieving this requires multidisciplinary collaboration and careful attention to clinical, technical, and ethical considerations. Full article

Esophageal squamous cell carcinoma (ESCC) accounts for the majority of esophageal cancers worldwide, with a poor prognosis and increasing resistance to conventional treatments. Faced with these limitations, nanoparticles (NPs) are attracting growing interest as innovative therapeutic agents capable of improving specificity and efficacy and reducing systemic toxicity. This study critically examines the pharmacological effects, mechanisms of action, and toxicity profiles of different metallic or organic nanoparticles tested on ESCC cell lines. Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 guidelines were followed by a meticulous literature search of Google Scholar, Web of Science, PubMed/Medline, and Scopus databases to achieve this goal. The results show that the anti-tumor properties vary according to the type of nanoparticle (copper(II) oxide (CuO), silver (Ag), gold (Au), nickel(II) oxide (NiO), nano-curcumin, etc.), the synthesis method (chemical vs. green), and the biological activity assessment method (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT), Bromodeoxyuridine (BrdU), Cell Counting Kit-8 (CCK8) assays, etc.). NPs derived from green synthesis, such as those based on Moringa oleifera, Photinia glabra, or pomegranate bark, exhibit moderate cytotoxic activity (50% inhibitory concentration (IC₅₀) between 92 and 500 µg/mL) but show good tolerance on normal cells. In contrast, chemically synthesized NPs, such as Cu(II) complexes with 1,3,5-benzenetricarboxylic acid (H₃btc) or 1,2,4-triazole (Htrz), show lower IC₅₀ (34–86 µM), indicating more marked cytotoxicity towards cancer cells, although data on their toxicity are sometimes lacking. In addition, multifunctional nanoparticles, such as gold-based nano-conjugates targeting Cluster of Differentiation 271 (CD271) or systems combined with doxorubicin, show remarkable activity with IC₅₀ below 3 µM and enhanced tumor selectivity, positioning them among the most promising candidates for future clinical application against ESCC. The most frequently observed mechanisms of action include induction of apoptosis (↑caspases, ↑p53, ↓Bcl-2), oxidative stress, and inhibition of proliferation. In conclusion, this work identifies several promising nanoparticles (silver nanoparticles derived from Photinia glabra (PG), gold-based nano-immunoconjugates targeting CD271, and silver–doxorubicin complexes) for future pharmaceutical exploitation against ESCC. However, major limitations remain, such as the lack of methodological standardization, insufficient in vivo and clinical studies, and poor industrial transposability. Future prospects include the development of multifunctional nanocomposites, the integration of biomarkers for personalized targeting, and long-term toxicological assessment. Full article

In today’s competitive digital landscape, application usability plays a critical role in user satisfaction and retention. Negative user reviews offer valuable insights into real-world usability issues, yet traditional analysis methods often fall short in scalability and contextual understanding. This paper proposes an intelligent framework that utilizes large language models (LLMs), including GPT-4, Gemini, and BLOOM, to automate the extraction of actionable usability recommendations from negative app reviews. By applying prompting and fine-tuning techniques, the framework transforms unstructured feedback into meaningful suggestions aligned with three core usability dimensions: correctness, completeness, and satisfaction. A manually annotated dataset of Instagram negative reviews was used to evaluate model performance. Results show that GPT-4 consistently outperformed other models, achieving BLEU scores up to 0.64, ROUGE scores up to 0.80, and METEOR scores up to 0.90—demonstrating high semantic accuracy and contextual relevance in generated recommendations. Gemini and BLOOM, while improved through fine-tuning, showed significantly lower performance. This study also introduces a practical, web-based tool that enables real-time review analysis and recommendation generation, supporting data-driven, user-centered software development. These findings illustrate the potential of LLM-based frameworks to enhance software usability analysis and accelerate feedback-driven design processes. Full article

Introduction: Craniotomy, a common neurosurgical procedure, is frequently associated with substantial perioperative challenges and delayed recovery. While Enhanced Recovery After Surgery (ERAS) protocols have demonstrated clear benefits in multiple surgical fields, their application in neurosurgery, particularly elective craniotomy, remains emerging. Objective: This systematic review and meta-analysis aimed to evaluate the efficacy and safety of ERAS protocols in adult patients undergoing elective craniotomy, focusing on key outcomes such as length of hospital stay (LOS), postoperative pain, complications, and functional recovery. Methods: Following PRISMA guidelines, a comprehensive search was conducted in PubMed, Embase, Scopus, Web of Science, and the Cochrane Library up to June 2025. Eligible studies included adult patients (≥18 years) undergoing elective craniotomy and compared ERAS protocols to conventional perioperative care. Primary outcomes were LOS, postoperative complications, pain, early oral intake, and early mobilization. Data extraction and risk of bias assessment (RoB 2.0) were independently performed by two reviewers. Results: Nine randomized controlled trials (RCTs), totaling 1453 patients, were included. Meta-analysis showed that ERAS protocols significantly reduced length of hospital stay (mean difference: −2.17 days; 95% CI: −2.92 to −1.42; p < 0.00001) and decreased the incidence of postoperative nausea and vomiting (odds ratio [OR]: 0.29; 95% CI: 0.19 to 0.44; I² = 0%). ERAS protocols were associated with higher odds of early mobilization (OR: 6.88; 95% CI: 3.46 to 13.68) and early oral intake (OR: 14.04; 95% CI: 7.80 to 25.26). Postoperative complications were significantly reduced in the ERAS group (OR: 0.49; 95% CI: 0.24 to 0.99; p = 0.048; I² = 0%). While early urinary catheter removal showed a favorable trend (OR: 13.48), high heterogeneity (I² = 95.7%) limits interpretability. Postoperative pain on day 1 did not differ significantly between groups (mean difference: −0.37; 95% CI: −2.38 to 1.63; p = 0.72). The overall risk of bias was rated low to moderate across studies. Conclusions: ERAS protocols in elective craniotomy are associated with shorter hospital stays, lower complication rates, reduced PONV, and earlier return to function, without increasing adverse events. These findings support broader implementation of ERAS in neurosurgical practice. Further multicenter RCTs are warranted to standardize and refine ERAS components for craniotomy. Full article

Background: Acacia catechu is an important traditional medicinal plant that has been used to manage several ailments. Many in vitro and in vivo studies have demonstrated that it exhibits chemopreventive and antineoplastic effects by modulating diverse signaling pathways and molecular targets involved in cancer progression. This review attempts to systematically investigate the anticancer mechanisms of A. catechu, encompassing antiapoptotic, antioxidant, and antiproliferative activities. Material and Methods: This review was conducted using scientific databases such as Scopus, Web of Science, and Google Scholar, covering the studies from 2000 to 2024. The PRISMA methodology was applied, using the keywords A. catechu, phytoconstituents, and cancer. Results: A total of 39 studies were compiled from various databases that cited the biological use of A. catechu. The plant has an abundance of phenolic compounds, including catechin, epicatechin, epigallocatechin-3-O-gallate, and epicatechin-3-O-gallate, which show strong anticancer activities. The anticancer potential of A. catechu is explained as it regulates several modulators like reactive oxygen species and cytokines, and downregulates oncogenic molecules like c-myc and various signaling pathways, such as c-Jun and NF-κB. Conclusions: Our findings suggest that A. catechu and its bioactive constituents have the potential for cancer prevention and therapy. However, further mechanistic investigations using pure compounds, along with preclinical and clinical trials, are essential to translate this potential into clinical applications. Full article

Water hyacinth (Eichhornia crassipes), native to South America, is a highly invasive aquatic plant threatening freshwater ecosystems worldwide. Its rapid proliferation negatively impacts water quality, biodiversity, and navigation. Remote sensing offers an effective means to monitor such aquatic environments by providing extensive spatial and temporal coverage with improved resolution. This systematic review examines remote sensing applications for monitoring water hyacinth and water quality in studies published from 2014 to 2024. Seventy-eight peer-reviewed articles were selected from the Web of Science, Scopus, and Google Scholar following strict criteria. The research spans 25 countries across five continents, focusing mainly on lakes (61.5%), rivers (21%), and wetlands (10.3%). Approximately 49% of studies addressed water quality, 42% focused on water hyacinth, and 9% covered both. The Sentinel-2 Multispectral Instrument (MSI) was the most used sensor (35%), followed by the Landsat 8 Operational Land Imager (OLI) (26%). Multi-sensor fusion, especially Sentinel-2 MSI with Unmanned Aerial Vehicles (UAVs), was frequently applied to enhance monitoring capabilities. Detection accuracies ranged from 74% to 98% using statistical, machine learning, and deep learning techniques. Key challenges include limited ground-truth data and inadequate atmospheric correction. The integration of high-resolution sensors with advanced analytics shows strong promise for effective inland water monitoring. Full article

Despite rapid progress in natural language processing, current interactive AI systems continue to struggle with interpreting ambiguous, idiomatic, and contextually rich human language, a barrier to natural human–computer interaction. Many deployed applications, such as language games or educational tools, showcase surface-level adaptation but do not systematically probe or advance the deeper semantic understanding of user intent in open-ended, creative settings. In this paper, we present Semantrix, a web-based semantic word-guessing platform, not merely as a game but as a living testbed for evaluating and extending the semantic capabilities of state-of-the-art Transformer models in human-facing contexts. Semantrix challenges models to both assess the nuanced meaning of user guesses and generate dynamic, context-sensitive hints in real time, exposing the system to the diversity, ambiguity, and unpredictability of genuine human interaction. To empirically investigate how advanced semantic representations and adaptive language feedback affect user experience, we conducted a preregistered 2 × 2 factorial study (N = 42), independently manipulating embedding depth (Transformers vs. Word2Vec) and feedback adaptivity (dynamic hints vs. minimal feedback). Our findings revealed that only the combination of Transformer-based semantic modelling and adaptive hint generation sustained user engagement, motivation, and enjoyment; conditions lacking either component led to pronounced attrition, highlighting the limitations of shallow or static approaches. Beyond benchmarking game performance, we argue that the methodologies applied in platforms like Semantrix are helpful for improving machine understanding of natural language, paving the way for more robust, intuitive, and human-aligned AI approaches. Full article

This study presents a comprehensive bibliometric review of 136 academic publications on smart grids, microgrids, and semiconductor technologies in the context of sustainable energy management. Data were collected from the Web of Science Core Collection and analyzed using VOSviewer to identify intellectual structures, thematic clusters, and research trajectories. The results demonstrate the increasing prominence of semiconductor-enabled solutions in advancing renewable energy integration, grid optimization, and energy storage systems. Five major research themes are identified: renewable energy and smart grid integration; distributed microgrid systems; optimization models; control strategies; and system-level resilience and cybersecurity. The analysis reveals a temporal evolution from foundational engineering (2020–2021) to intelligent, digitally enhanced energy systems (2022–2025), with a growing emphasis on electric mobility, digital twins, and advanced energy management techniques, such as convex optimization. Beyond mapping trends, this study underscores critical research gaps in the non-English literature, multi-database integration, and practical deployment. The findings provide actionable insights for researchers, policymakers, and industry leaders by highlighting technological maturity, real-world applications, and strategic implications for energy transition. By aligning digital intelligence, semiconductor innovation, and sustainable energy goals, this review advances a forward-looking agenda for resilient and equitable energy systems. Full article

Cancer, a leading global cause of death responsible for nearly 10 million deaths annually, demands innovative therapeutic strategies. Intrinsic cytotoxicity and biocompatibility of zinc oxide nanoparticles (ZnO-NPs) have rendered them promising nanoplatforms in oncology. We herein systematically review their applications for targeted cancer chemotherapy, with a focus on physicochemical properties, drug delivery mechanisms, and interactions with the tumor microenvironment (TME). We searched PubMed, SCOPUS, and Web of Science from inception through December 2024 for peer-reviewed preclinical studies on cancer models. Results were qualitatively synthesized. Quality was assessed with the SYRCLE risk of bias tool. Among 20 eligible studies, ZnO-NPs were frequently functionalized with ligands to enhance tumor targeting and minimize systemic toxicity. Chemotherapeutic agents (doxorubicin, 5-fluorouracil, docetaxel, cisplatin, gemcitabine, and tirapazamine) were loaded into ZnO-based carriers, with improved anticancer efficacy compared to free drug formulations, particularly in multidrug-resistant cell lines and in vivo murine xenografts. The mildly acidic TME was exploited for pH-responsive drug release, premature leakage reduction, and improvement of intratumoral accumulation. Enhanced therapeutic outcomes were attributed to reactive oxygen species generation, zinc ion-mediated cytotoxicity, mitochondrial dysfunction, and efflux pump inhibition. Deep tumor penetration, apoptosis induction, and tumor growth suppression were also reported, with minimal toxicity to healthy tissues. ZnO-NPs might constitute a versatile and promising strategy for targeted cancer chemotherapy, offering synergistic anticancer effects and improved safety profiles. Future studies emphasizing long-term toxicity, immune responses, and scalable production could lead to clinical translation of ZnO-based nanomedicine in oncology. Full article

Electromagnetic induction (EMI) devices have become increasingly popular for their soil bulk properties, soil nutrient status, and use in taking non-invasive soil salinity measurements. However, the high cost of data acquisition (DAQ) systems has been a significant barrier to the widespread adoption of these devices. In this study, we addressed this challenge by developing a cost-effective, easy-to-use, open-source DAQ system, transferable to the end user. This system employs a Raspberry Pi 4 model, paired with various components, to monitor the speed and position of the EM38 (Geonics Ltd, Mississauga, ON, Canada) and compare these with a proprietary CR1000 system. Through our results, we demonstrate that the low-cost DAQ system can successfully extract the analogical signal from the device, which is strongly responsive to the variation in the soil’s physical properties. This cost-effective system is characterized by increased flexibility in software processes and provides performance comparable to the proprietary system in terms of its geospatial data and ECb measurements. This was validated by the strong correlation (R² = 0.98) observed between the data collected from both systems. With our zoning analysis, performed using the Kriging technique, we revealed not only similar patterns in the ECb data but also similar patterns to the Normalized Difference Vegetation Index (NDVI) map, suggesting that soil physical characteristics contribute to variability in crop vigor. Furthermore, the developed web application enabled real-time data monitoring and visualization. These findings highlight that the open-source DAQ system is a viable, cost-effective alternative for soil property monitoring in precision farming. Future enhancements will focus on integrating additional sensors for plant vigor and soil temperature, as well as refining the web application, supporting zone classification based on the use of multiple parameters. Full article